A rapid imaging processing method for horizontal well logging while drilling tracking

Abstract

The present invention discloses a rapid imaging processing method for horizontal well logging while drilling tracking, comprising the following steps: 1) determining the bit penetration depth correction points in the horizontal well trajectory according to the collected dynamic information of logging while drilling; 2) assuming the target points and depth correction points in the horizontal well trajectory as multiple vertical wells to construct multi-well information; 3) obtaining a corrected depth-domain structural model by using the multi-well information; 4) establishing an error surface by using the corrected depth-domain structural model and multi-well target stratification; 5) rapidly obtaining anisotropic parameters by using the error surface through travel-time constant tomography; 6) establishing a high-precision TTI anisotropic velocity model by using the anisotropic velocity field; 7) performing migration processing on the imaging results; 8) correcting the logging while drilling tracking guidance model according to the seismic data of the migration imaging results. The method of the present invention provides geophysical exploration support for oilfield exploration and development and improves the drilling encounter success rate.

Description

Technical Field

[0001] The present invention relates to geophysical technologies, and in particular to a rapid imaging processing method based on horizontal well logging while drilling (LWD) tracking. Background Art

[0002] Shale gas reservoirs are densely and continuously distributed. In order to improve the reservoir encounter rate of shale gas wells and the production of shale gas wells, horizontal wells are generally adopted in the well type design for shale gas development. High-precision prediction of the burial depth of shale gas reservoirs and the dip angle of reservoir formations places higher requirements on the imaging accuracy of seismic data.

[0003] In the areas for shale gas exploration and development, the surface conditions are complex and changeable, the underground structures are complex, steeply dipping formations are developed, and the longitudinal and transverse velocity changes are drastic. Therefore, velocity modeling is difficult and high requirements are imposed on the applicability of imaging algorithms. At the same time, since the isotropic processing method cannot accurately describe anisotropic velocity, errors exist between the imaging depth and attitude of the formation and the actual situation, which affects the accurate target entry of horizontal well drilling and the efficient traversing of the target layer. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a rapid imaging processing method based on horizontal well LWD tracking in view of the defects in the prior art.

[0005] The technical solution adopted by the present invention to solve its technical problems is as follows: A rapid imaging processing method based on horizontal well LWD tracking includes the following steps:

[0006] 1) According to the collected LWD drilling dynamic information, analyze the formation layers traversed by the well trajectory, and determine the bit penetration depth correction points in the horizontal well trajectory by using small layer comparison and interactive analysis of the tracking model; the LWD drilling dynamic information includes surface measurement data, downhole measurement data, and external data, which are obtained in real time through the LWD guidance model.

[0007] 2) Assume the target points and depth correction points in the horizontal well trajectory as multiple vertical wells, and based on the tectonic deformation mechanism and geostatistics, assume a virtual well more than 1 km away before the horizontal well drilling to construct multi-well information.

[0008] 3) Use the multi-well information in step 2) to make synthetic seismograms, calibrate the formation horizons of the existing depth domain data, and obtain a corrected depth domain structural model.

[0009] 4) Use the corrected depth domain structural model and multi-well target layering, and combine the LWD tracking comprehensive information. Through geostatistics, establish an error surface for interpolation and extrapolation of well-seismic errors along the layer, and depict the velocity and anisotropic parameter changes of geological anomalies and micro-scale structures. The LWD tracking comprehensive information is obtained in real time through the LWD guidance model.

[0010] The details are as follows:

[0011] 4.1) Conduct horizon calibration based on multi-well target layer information on the isotropic depth migration results, pick up horizons, and establish a depth-domain structural model; and calculate the depth error between the seismic imaging at the well point location and the well-layer stratification.

[0012] 4.2) For the area without wells, set the stratification of virtual wells according to geological data, geophysical exploration data, and adjacent well data, and calculate the depth error value.

[0013] 4.3) Extrapolate the well-seismic error along the depth-domain structural model by the geostatistical method to form an error surface.

[0014] 5) Utilize the error surface, the depth-domain structural model corrected by LWD guidance, and the previous benchmark velocity model to quickly calculate anisotropic parameters: delta body, epsilon body, and anisotropic velocity field through travel-time constant tomography.

[0015] Establish a travel-time constant tomography matrix based on the error surface, and solve the matrix to obtain TTI anisotropic parameters.

[0016] 6) Utilize the anisotropic velocity field to establish a high-precision TTI anisotropic velocity model.

[0017] The details are as follows:

[0018] Perform time-depth conversion on the pre-drill time-domain seismic data using the anisotropic velocity field, extract the seismic attributes dip and azimuth on this data volume, establish a sparse matrix and solve it for the LWD processing area with a small grid of 100m * 100m * 10m through model-based tomography constrained by multi-well information and anisotropic attributes, and finally establish a high-precision TTI anisotropic velocity model.

[0019] 7) Perform migration processing on the imaging results obtained from the high-precision TTI anisotropic velocity model to obtain a migration imaging result closer to the real situation.

[0020] 8) Predict the geological conditions in front of the drill bit based on the seismic data of the migration imaging result, and correct the LWD tracking guidance model; and predict the target depth and formation attitude according to the corrected LWD tracking guidance model, adjust the horizontal well trajectory in real time, and obtain updated LWD dynamic information and comprehensive information.

[0021] The beneficial effects produced by the present invention are:

[0022] By adopting the velocity modeling method of the multi-well information constraint combined with the geological information statistical method, the present invention improves the modeling accuracy of the anisotropic velocity model, can effectively solve the well-seismic depth error and local false micro-structure problems caused by anisotropy, improves the accuracy of the depth-domain migration results, ensures the rationality of the real-time drilling tracking and guiding model, timely solves the difficulties encountered in the drilling process, and guarantees the drilling encounter rate of high-quality layers. Description of the Drawings

[0023] The present invention will be further described below in conjunction with the drawings and embodiments. In the drawings:

[0024] Figure 1 is the method flow chart of the embodiment of the present invention;

[0025] Figure 2 is the schematic diagram of the depth-domain structural model using integrated information processing and interpretation collaborative correction in the embodiment of the present invention;

[0026] Figure 3 is the schematic diagram of the error surface established by the geological statistical method for interpolating and extrapolating the well-seismic error along the layer in the embodiment of the present invention;

[0027] Figure 4 is the comparison diagram of quickly establishing the anisotropic velocity field based on the travel-time constant tomography technology in the embodiment of the present invention;

[0028] Figure 5 is the statistical chart of the well depth coincidence relationship between the seismic data and the known drilling data in the embodiment of the present invention. Detailed Embodiment

[0029] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0030] As Figure 1 shown, a rapid imaging processing method based on real-time drilling tracking of horizontal wells includes the following steps:

[0031] 1) Determine the size of the velocity field for real-time processing and the migration processing range according to the depth, offset aperture, and bin of the actual drilled bit's horizontal travel, and delimit the real-time processing area;

[0032] According to the collected real-time drilling tracking dynamic information, analyze the layers traversed by the well trajectory, and use small-layer comparison and tracking model interactive analysis to determine the drill bit penetration depth correction points in the horizontal well trajectory; the real-time drilling tracking dynamic information includes surface measurement data, downhole measurement data (geological data, trajectory data, drilling data) and external data (adjacent well data, block data, empirical data), and is obtained through the real-time drilling tracking and guiding model;

[0033] 2) As Figure 2 shown, assume the target points and depth correction points in the horizontal well trajectory as multiple vertical wells, and in combination with geological personnel, based on the tectonic deformation mechanism and geostatistics, assume a virtual well more than 1 km away before the horizontal well is drilled; and obtain multi-well information of the structure: adjacent well data, block geological information, empirical data;

[0034] 3) Use the multi-well information in step 2) to make synthetic seismograms, calibrate horizons for the existing depth-domain data, and obtain a corrected depth-domain structural model together with the interpreters;

[0035] 4) As Figure 3 shown, use the corrected depth-domain structural model and multi-well target horizons, combine with the comprehensive information of logging while drilling, and establish an error surface for interpolation and extrapolation of well-seismic errors along the horizon through geostatistics, and depict the velocity and anisotropic parameter changes of geological anomalies and micro-structures; the comprehensive information of logging while drilling is obtained in real time through the logging while drilling guidance model;

[0036] Specifically as follows:

[0037] 4.1) Conduct horizon calibration on the isotropic depth migration results according to the multi-well target horizon information, pick up horizons, and establish a depth-domain structural model; and calculate the depth errors between seismic imaging at the well point location and the well horizon stratification;

[0038] 4.2) For the area without wells, set the stratification of the virtual well according to geological data, geophysical exploration data, and adjacent well data, and calculate the depth error value;

[0039] 4.3) Extrapolate the well-seismic errors along the depth-domain structural model by geostatistics to form an error surface;

[0040] 5) Use the error surface, the depth-domain horizon model corrected by logging while drilling guidance, and the previous benchmark velocity model to quickly calculate the anisotropic parameters through travel-time constant tomography technology: delta body, epsilon body, anisotropic velocity field, as Figure 4 shown;

[0041] 6) Use the anisotropic velocity field and the five anisotropic parameters of stable isotropic velocity (the migration velocity of the actual drilled seismic data volume) VP0, ε, δ, θ, φ to establish a high-precision TTI anisotropic velocity model;

[0042] Specifically as follows:

[0043] Perform time-depth conversion on pre-drilling time-domain seismic data using an anisotropic velocity field. Extract seismic attributes Dip and Azimuth from this data volume. Divide the area for while-drilling processing into small grids of 100m * 100m * 10m by model tomography method constrained by multi-well information and anisotropic attributes, establish a sparse matrix and solve it, and finally establish a high-precision TTI anisotropic velocity model;

[0044] 7) The obtained high-precision anisotropic velocity model is used to quickly calculate seismic imaging within several kilometers near the well. Multiple high-precision imaging processes such as TTI Kirchhoff depth migration and TTI-RTM can be selected according to actual situations to obtain a more accurate migration imaging result closer to the truth;

[0045] 8) Predict geological conditions such as faults and sudden changes in attitude in front of the drill bit based on the seismic data of the migration imaging result, and correct the while-drilling tracking guidance model; and predict the target depth and formation attitude based on the corrected while-drilling tracking guidance model, adjust the horizontal well trajectory in real time, and obtain updated while-drilling tracking drilling dynamic information and comprehensive information.

[0046] In the method of the present invention during while-drilling tracking, with the change in the understanding of the target area structure, the wellbore trajectory design is adjusted in a timely manner, mainly by adjusting the target depth of the target point. The accuracy of target depth prediction is the core element to ensure accurate target entry. To a certain extent, the processing results of while-drilling seismic data determine whether accurate target depth can be obtained in while-drilling guidance.

[0047] The method of the present invention combines actual drilling data, uses well-controlled TTI anisotropic velocity modeling technology to quickly correct the velocity model, and performs fast imaging processing using the travel-time constant tomography technology based on the model. Submit high-precision migration imaging results within 5 kilometers of the target well area within 48 hours, provide a reliable geophysical guidance model for while-drilling tracking guidance, and timely solve the difficulties encountered during the drilling process.

[0048] Figure 5 It is the statistical chart of the depth error of the actual drilling correction points before and after F4 while-drilling fast imaging.

[0049] It should be understood that for those of ordinary skill in the art, improvements or transformations can be made according to the above description, and all such improvements and transformations should fall within the protection scope of the appended claims of the present invention.

Claims

1. A rapid imaging processing method for follow - up while drilling in horizontal wells, characterized in that Including the following steps: 1) Analyze the formation layers traversed by the horizontal well trajectory based on the collected drilling dynamic information while drilling, and use small layer comparison and interactive analysis of the tracking model to determine the bit penetration depth correction points in the horizontal well trajectory; the drilling dynamic information while drilling includes surface measurement data, downhole measurement data, and external data, which are obtained in real time through the drilling tracking and guiding model; 2) Assume the target points and depth correction points in the horizontal well trajectory as multiple vertical wells, and set virtual wells as needed to construct multi-well information; 3) Use the multi-well information in step 2) to make synthetic seismograms, calibrate the horizons for the existing depth-domain data, and obtain a corrected depth-domain structural model; 4) Use the corrected depth-domain structural model to layer the multi-well target points, combine with the comprehensive information while drilling, establish an error surface, and depict the velocity and anisotropic parameter changes of geological anomalies and microstructural scales; the comprehensive information while drilling is obtained in real time through the drilling tracking and guiding model; 5) Use the error surface, the depth-domain structural model corrected by drilling guidance, and the previous benchmark velocity model to quickly obtain anisotropic parameters: delta body, epsilon body, anisotropic velocity field through travel-time constant tomography; Establish a travel-time constant tomography matrix based on the error surface, and solve the matrix to obtain TTI anisotropic parameters; 6) Use the anisotropic velocity field to establish a high-precision TTI anisotropic velocity model; 7) Perform migration processing on the imaging results obtained from the high-precision TTI anisotropic velocity model to obtain a migration imaging result that is closer to the real one; 8) Predict the geological conditions in front of the bit based on the seismic data of the migration imaging result, and correct the drilling tracking and guiding model; and based on the corrected drilling tracking and guiding model, predict the target depth and formation attitude, and adjust the horizontal well trajectory in real time to obtain updated drilling dynamic information and comprehensive information while drilling.

2. The rapid imaging processing method based on horizontal well logging while drilling according to claim 1, wherein In step 1), the determination of the bit penetration depth correction points in the horizontal well trajectory is to analyze the formation layers traversed by the horizontal well trajectory based on the collected drilling dynamic information while drilling, and use small layer comparison and interactive analysis of the tracking model to determine the depth correction points.

3. The rapid imaging processing method based on horizontal well logging while drilling according to claim 1, characterized in that, In step 2), the virtual well is set according to the tectonic deformation mechanism and geostatistics, and a virtual well is assumed more than 1 km before the horizontal well drilling.

4. The method for rapid imaging processing based on horizontal well logging while drilling according to claim 1, characterized in that, The specific method for establishing the error surface in step 4) is as follows: 4.1) Conduct horizon calibration on the isotropic depth migration results based on the multi-well target point stratification information, pick up horizons, and establish a depth-domain structural model; and calculate the depth error between the seismic imaging at the well point position and the well position stratification; 4.2) For the area without wells, set the stratification of the virtual well according to geological data, geophysical exploration data, and adjacent well data, and calculate the depth error value; 4.3) Extrapolate the well-seismic error along the depth-domain structural model using geostatistics to form an error surface.

5. The rapid imaging processing method based on real-time tracking while drilling in horizontal wells according to claim 1, wherein In step 6), a high-precision TTI anisotropic velocity model is established; Specifically as follows: Perform time-depth conversion on pre-drilling time-domain seismic data using the anisotropic velocity field. Extract the seismic attributes Dip and Azimuth from this data volume. Establish a sparse matrix and solve it for a small grid of 100m * 100m * 10m in the processing area while drilling through model tomography method constrained by multi-well information and anisotropic attributes. Finally, establish a high-precision TTI anisotropic velocity model.

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